Impaired Middle Cerebral Artery and Cerebrovascular Vasodilation in Healthy Aging
Bibliographic record
Abstract
This study quantified the effect of age on cerebrovascular reactivity (CVR) and cerebrovascular conductance. Cerebral blood flow velocity (CBFV; transcranial Doppler (TCD)) and cross‐sectional area (CSA) of the middle cerebral artery (MCA; 3T Magnetic Resonance Imaging (MRI)) to measure cerebral blood flow (CBF) (HC; 6% CO 2 ) were measured in healthy populations of young (YA: n=6, 24 ± 2 years, 3 males) and older adults (OA: n=6, 66 ± 6 years, 1 male) during hypercapnia (HC). CVR was calculated as the percent change (%Δ) in CBF per change in end tidal carbon dioxide where the end tidal carbon dioxide was the average of the change during TCD and the MRI studies which were performed on separate days. Cerebrovascular conductance was calculated as the quotient of CBF and mean arterial pressure. CVR was not different between YA (2.0 ± 0.9 at minute one to 3.1 ± 0.9 %/mmHg at minute four) and OA (2.6 ± 1.2 at minute one to 4.0 ± 1.6 %/mmHg at minute four) over the four minutes of HC. The CSA in YA increased from 6.29 ± 0.71 at baseline to 7.16 ± 1.00 mm 2 at minute four while OA increased from 6.36 ± 0.92 at baseline to 6.75 ± 1.27 mm 2 at minute four (p=0.5). The %ΔCBF was not different between the YA and OA yet the %Δ in CBFV was greater during HC in the OA (p=0.01). However, when %ΔCBFV was normalized to the change in mean arterial pressure there was no longer a difference between YA and OA. Also, cerebrovascular conductance was greater in the YA (p=0.03). Therefore, a greater blood pressure response in the OA may be responsible for their preserved CVR despite evidence for an age‐related impairment in MCA dilation and vascular conductance. Supported by the Canadian Institutes of Health Research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".